Length-encoded rod-shaped magnetic particle-based multipurpose immuno- and molecular assay system for rapid and accurate diagnostics: VEUS
Bibliographic record
Abstract
• VEUS is a point-of-care test platform with precision diagnostic capability. • VEUS exhibits equivalent power to clinical diagnosis for traumatic brain injury. • VEUS utilizes length-encoded rod-shaped magnetic particles for multiplexing. • VEUS offers complete automation from assays to image processing. • VEUS possesses circumferentially focusing illumination at 1× magnification. In the biomedical field, there is increasing demand to combine the capabilities of POCTs with precision diagnostics for timely medical interventions. While precision diagnostics provide high sensitivity and multiplexing, they require at least a day for turnaround and involve costly, bulky equipment and skilled operators. Existing POCTs face challenges in concurrently achieving high sensitivity, accuracy, multiplex capability, and rapidity. A platform enabling immuno- and molecular diagnostics would enhance diagnostic and prognostic precision. We present VEUS (Versatile, Easy, User-friendly System), which integrates the benefits of POCTs and precision diagnostics. VEUS features four key components: 1) full automation of the assay workflow, 2) circumferentially focusing illumination with 1× imaging, 3) high-throughput AI-based decoding and image processing, and 4) length-encoded rod-shaped magnetic particles. These innovations yield ten times higher optical sensitivity than conventional microscopy and offer a universal, ready-to-commercialized platform capable of performing both immuno- and molecular diagnostics. Demonstrating broad clinical applications, we present this dual feature by its application in diagnosing traumatic brain injury and sepsis using immunoassays and respiratory viral infections by a molecular assay based on the CRISPR/Cas system. VEUS has the potential to significantly improve patient outcomes by providing rapid, accurate diagnostics at the point of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".